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A theoretical model for the ethical use of generative AI tools in higher education assessments

Primary research

#1514

T1new
Topic
unassigned (set during synthesis)
First seen
2026-08-09 07:16:03
Last seen
2026-08-09 07:16:03

Source raw items (1)

  • Semantic Scholar2026-08-09 07:15:27
    A theoretical model for the ethical use of generative AI tools in higher education assessments

    This study aims to develop a theoretical framework to identify the key factors influencing the ethical use of generative artificial intelligence (GenAI) tools in academic assessments from a student perspective. Grounded in digital literacy theory and self-determination theory (SDT), the study examines six constructs: GenAI transparency, GenAI autonomy, GenAI privacy, GenAI ethics literacy, GenAI policy clarity and GenAI fairness. Data was collected from 171 university students through an online survey and analyzed using partial least squares structural equation modeling (PLS-SEM). The results show that GenAI transparency, GenAI privacy, GenAI ethics literacy, GenAI policy clarity and GenAI fairness significantly influence the ethical use of GenAI tools in assessments, while GenAI autonomy did not have a significant effect. The findings provide actionable insights for students, educators, institutions and developers to promote the ethical use of GenAI tools in academic settings. Establishing clear policies, strengthening ethics literacy and ensuring fairness and transparency can foster responsible integration of GenAI in assessments. This study is original in its focus on the ethical use of GenAI in academic assessments from a student perspective, a dimension largely overlooked in prior research. By integrating under-explored constructs such as policy clarity, fairness and ethics literacy, the study contributes a novel theoretical framework and empirical evidence to guide students’ responsible use of GenAI and inform institutional practices for ethical GenAI adoption.